Maswali ya Kusimbua ya Angalia mbele

Majibu na maelezo huonekana unapojibu maswali.

1. Ni nini hutofautisha usimbaji wa kuangalia mbele na usimbaji wa kawaida wa kubahatisha?
   • Inahitaji mifano miwili iliyofunzwa tofauti
   • Haitumii muundo wa rasimu tofauti - tu muundo unaolengwa wenyewe
   • Inafanya kazi kwa kazi za utafsiri pekee
   • Inakadiria kabisa mfano
2. Je, ni njia gani ya nambari inayosisitiza usimbaji wa kuangalia mbele?
   • Kushuka kwa gradient
   • Mabadiliko ya Fourier
   • K-inamaanisha kuunganisha
   • Jacobi (fixed-point) iteration
3. Je, ni matawi gani mawili yanayofanana ambayo yanatembea kwa kila hatua?
   • Tawi la mafunzo na tawi la majaribio
   • Kisimbaji na avkodare
   • Tawi la kuangalia mbele na tawi la uthibitishaji
   • Muundo wa rasimu na modeli inayolengwa
4. Ni nini kimehifadhiwa kwenye bwawa la 'n-gram'?
   • Mipangilio inayowezekana ya ishara nyingi inayoonekana wakati wa marudio sambamba
   • historia ya mazungumzo ya mtumiaji
   • Uzito wa mfano
   • Masks ya tahadhari
5. What supports correctness when lookahead decoding commits a guessed n-gram prefix?
   • Using the same model makes every unverified guess correct
   • Every candidate in the n-gram pool must be committed
   • Verification against the target's greedy predictions or an appropriate distribution-preserving sampling procedure
   • A larger window proves the generated statements are true
6. What resource tradeoff is important when deploying lookahead decoding on already busy hardware?
   • Candidate generation uses no compute or memory
   • Additional parallel work may offset the benefit of fewer sequential passes
   • Every larger window must lower wall-clock latency
   • The target weights are automatically compressed to zero size
7. Why should a lookahead candidate remain provisional until target verification?
   • Parallel iteration guesses can differ from the target's valid continuation
   • Every cached n-gram has already been proven correct by storage alone
   • The lookahead branch permanently changes the target weights
   • Verification is needed only to determine the output filename
8. Does a standard training-free lookahead iteration update the target model's learned weights?
   • Yes; each candidate applies a gradient update to every parameter
   • Yes; verified n-grams become a new training dataset immediately
   • Only the tokenizer's vocabulary is retrained at every step
   • No; it changes candidate token states and verifies outputs while keeping the model weights fixed